In this manuscript a novel approach for SAR urban change detection is presented. Its peculiarity is its ability to detect the changes not directly from the measured amplitude data, but exploiting the whole complex image. In particular, the scene in modelled as a Local Gaussian Markov Random Field, and is described via the so called hyperparameters, which refers to the spatial correlation of pixels. By comparing such hyperparameters obtained from a pre-event and a post-event dataset, we can detect occurred changes. Results on real datasets show good detection accuracy together with very low false alarm rate.
SAR change detection in a Markovian Bayesian framework
BASELICE, FABIO;FERRAIOLI, GIAMPAOLO;PASCAZIO, Vito
2013-01-01
Abstract
In this manuscript a novel approach for SAR urban change detection is presented. Its peculiarity is its ability to detect the changes not directly from the measured amplitude data, but exploiting the whole complex image. In particular, the scene in modelled as a Local Gaussian Markov Random Field, and is described via the so called hyperparameters, which refers to the spatial correlation of pixels. By comparing such hyperparameters obtained from a pre-event and a post-event dataset, we can detect occurred changes. Results on real datasets show good detection accuracy together with very low false alarm rate.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.